OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series

Wu, Youxi; Hu, Qian; Li, Yan; Guo, Lei; Zhu, Xingquan; Wu, Xindong

doi:10.1109/tcyb.2022.3169327

Cited by 21 publications

(8 citation statements)

References 54 publications

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“…To compress frequent patterns, Top-k SPM (28,29), closed SPM (30), and maximal SPM (31,32) were investigated. With the development of SPM, various SPM methods have been investigated for different mining tasks, such as three-way SPM (33,34), weak-gap SPM (35,36), high utility SPM (37,38), spatial co-location pattern mining (11), order-preserving SPM (16), and contrast SPM (12,14). For example, threeway SPM can effectively improve the mining speed and avoid large deviations by dividing the characters into three types: strong, medium, and weak.…”

Section: Related Workmentioning

confidence: 99%

“…Sequential pattern mining (SPM) (1,2), as an important knowledge discovery methods (3,4,5), focuses on finding interesting subsequences (called patterns) in sequences or sequence databases (SDBs). Various SPM methods have been investigated, such as SPM with gap constraints (6,7), high-utility SPM (8,9,10), contrast SPM (12,14,13,15), order-preserving SPM for time series (16), and closed SPM (17,18,19). However, most of these studies focuses on mining events (with each event corresponding to a character) that have occurred, which is called positive SPM (20,21).…”

Section: Introductionmentioning

confidence: 99%

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One-off Negative Sequential Pattern Mining

Wu¹,

Chen²,

Li³

et al. 2022

Preprint

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Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this paper discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this paper proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency, but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One-off Negative Sequential Pattern Mining

Wu¹,

Chen²,

Li³

et al. 2022

Preprint

Self Cite

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“…To overcome the drawbacks of the symbolization methods, our previous work proposed the OPP mining method which does not need to symbolize the time series [17]. To effectively discover the frequent OPPs, OPP-Miner was proposed and employed an OPP matching method to calculate the supports.…”

Section: Related Workmentioning

confidence: 99%

“…occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by order-preserving matching, our previous work proposed the order-preserving pattern mining (OPP-Miner) algorithm [17], which used the relative order of real values to express a pattern called an order-preserving pattern (OPP). By mining OPPs, we can find frequent trends in a time series.…”

Section: Introductionmentioning

confidence: 99%

“…By mining OPPs, we can find frequent trends in a time series. An illustrative example is shown in However, there are two problems with OPP mining: (i) OPP-Miner [17] adopts a pattern matching method to calculate pattern support. Although the space complexity of OPP-Miner is low, its efficiency is also low, since it does not use the calculation results of the sub-patterns.…”

Section: Introductionmentioning

confidence: 99%

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OPR-Miner: Order-preserving rule mining for time series

Wu¹,

Zhao²,

Li³

et al. 2022

Preprint

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Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance.

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